Wrong-drug and wrong-patient errors occur at a rate of roughly one per thousand orders in inpatient and outpatient settings, resulting in millions of potentially harmful errors annually in the US. Accurate problem lists help prevent wrong-drug and wrong-patient errors by allowing the electronic medical record (EMR) to remind prescribers when orders do not match the problem list. Unfortunately, problem lists are often inaccurate. Indication alerts prompt prescribers to add new problems to the problem list when a drug order does not match the problem list. Indication alerts also promote self-interception of wrong-drug and wrong-patient errors by increasing situation awareness. Two types of self-interception events can be measured in an automated way: (a) abandon-and-reorder?a prescriber starts then abandons an incorrect order before signing it, and then re- orders for the correct drug or patient; or (b) retract-and-reorder?a prescriber cancels an incorrect order soon after signing it, and then re-orders for the correct drug or patient. Previous work used the abandon-and-reorder and retract-and-reorder methods to measure the effectiveness of several interventions, including indication alerts, in reducing wrong-drug and wrong-patient errors, but that work was limited. First, only a small number of drugs were studied. Second, prior studies were done at a single medical center and involved only one commercial EHR. Third, until 2016, there was no validated, National Quality Forum-endorsed instrument for estimating the rate of wrong-patient orders. Fourth, the prior studies of indication alerts used posttest only designs and therefore could not test for an increase in the self-interception rate over baseline. The proposed project addresses these limitations in the earlier work and fills important gaps in knowledge about how to prevent wrong-drug and wrong-patient errors and how to improve the completeness of problem lists. The project's Specific Aims are: 1. At one hospital in Chicago and six in New York City, using two commercial EMR systems, implement a set of 30-50 indication alerts for medications that are vulnerable to look-alike and sound-alike errors. 2. Using an interrupted time series study design, quantify the effect of indication alerts on (a) the combined rate of self-intercepted wrong-drug and wrong-patient computerized prescriber order entry (CPOE) errors and (b) on the rate of each type of error viewed separately. It is predicted that indication alerts will increase the combined rate of self-intercepted wrong-drug and wrong-patient errors by roughly 25%, from 158 to 196 events per 100,000 orders, and will increase the self-interception rate of each type when viewed separately, as measured by an increase in the sum of abandon-and-reorder and retract-and-reorder events. 3. Assess the impact of indication alerts on the probability of adding new diagnoses to the problem list during encounters that include CPOE. It is predicted that indication alerts will double the likelihood that a problem is placed on the problem list during encounters that include CPOE, with new problems being placed during 12% of pre-intervention orders and 25% of post-intervention orders. The intervention should add to knowledge and improve quality and patient safety.